Elucidating thermochemical pretreatment effectiveness of different particle-size switchgrass for cellulosic ethanol production

Effects of switchgrass particle sizes (\u3c0.25 mm, 0.5–1.0 mm, and 2.0–4.0 mm) on the effectiveness of H2SO4 and NaOH pretreatments were investigated. As particle size increased, glucan, xylan, and lignin contents in raw switchgrass augmented from 30.32% to 32.02%, 18.44% to 19.03%, and 14.78% to 15.33%, respectively. Glucan and xylan (58.54–60.94% and 18.55–20.01%) contents in NaOH pretreated switchgrass and their recoveries (91.95–94.69% and 47.91–52.31%) increased. The highest glucan content (55.76%) and recovery (79.72%) in H2SO4 pretreated switchgrass were reached by middle particle size. The lowest (59.39% for H2SO4 and 58.99% for NaOH) and highest (65.23% for H2SO4 and 66.15% for NaOH) CrI values were obtained from middle and small particle sizes, respectively. SEM images and FTIR spectra showed no visible variations in microstructures and chemical bonds among different particle sizes under the same pretreatment conditions. On the basis of pretreated switchgrass, the highest ethanol concentration and efficiency were reached by big particle size for H2SO4 pretreated (7.03 g/L and 49.28%) switchgrass, while they were achieved by small particle size for NaOH pretreated (11.68 g/L and 72.37%) switchgrass. The highest ethanol yield based on raw switchgrass was attained by big particle size for untreated (29.54%), middle particle size for H2SO4 pretreated (30.60%), and small particle size for NaOH pretreated (62.36%) switchgrass. These findings indicate that the optimal ethanol conversion performance is the result of the interaction between the pretreatment method and biomass particle size

Secondary frequency control of islanded microgrid considering wind and solar stochastics

As the high penetration of wind and photovoltaic distributed generation (DG) in the microgrid, the stochastic and low inertia emerge, bringing more challenges especially when the microgrid operates in isolated islands. Nevertheless, the reserve power of DGs in deloading control mode can be utilized for frequency regulation and mitigating frequency excursion. This paper proposed a model predictive control (MPC) secondary frequency control method considering wind and solar power generation stochastics. The extended state-space matrix including unknown stochastic power disturbance is established, and a Kalman filter is used to observe the unknown disturbance. The maximum available power of wind and solar DGs is estimated for establishing real-time variable constraints that prevent DGs output power from exceeding the limits. Through setting proper weight coefficients, wind and photovoltaic DGs are given priority to participate in secondary frequency control. The distributed restorative power of each DG is obtained by solving the quadratic programming(QP) optimal problem with variable constraints. Finally, a microgrid simulation model including multiple PV and wind DGs is built and performed in various scenarios compared to the traditional secondary frequency control method. The simulation results validated that the proposed method can enhance the frequency recovery speed and reDGce the frequency deviation, especially in severe photovoltaic and wind fluctuations scenarios.Comment: Accepted by Acta energiae solaris sinica [In Chinese

Zhang Taiyan\u27s Visim on East Asian Union

Normally, we know that Zhang Taiyan is the pioneer of anti-Man nation and an important founder of RPC; however, his idea on building an East-Asia Union has not been known. This article argues that Zhang initiates his theory on the East Asian Union after the Sino-Japanese War in 1894. Zhang actually completed this idea when he was in Taiwan; he escaped to there after the unsuccessful Hundred-Day Reform. In addition, Zhang\u27s idea on building the East Asia Union is showing in his 訄書, and that is different from related desires of Kang Youwei and Sun Yatsen. Therefore, Zhang is not a nationalist; his slogan of anti-Man nation was only a convenient method.文部科学省グローバルCOEプログラム 関西大学文化交渉学教育研究拠点東アジアの思想と構

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model’s predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (R2) and root mean square error (RMSE) of 0.9989 and 6.9, respectively, which were significantly better than those of the other two models. Results of validation experiments showed that the detection system met the requirements of moisture content online detection in the drying process of agricultural products. The R2 and RMSE were 0.9901 and 1.47, respectively, indicating the good performance of the model combining multi-sensor fusion and CNN in moisture content online detection for agricultural products in the drying process. The moisture content online detection system established in this study is of great significance for researching new drying processes and realizing the intelligent development of drying equipment. It also provides a reference for online detection of other indexes in the drying process of agricultural products

Model predictive control strategy in waked wind farms for optimal fatigue loads

With the rapid growth of wind power penetration, wind farms (WFs) are required to implement frequency regulation that active power control to track a given power reference. Due to the wake interaction of the wind turbines (WTs), there is more than one solution to distributing power reference among the operating WTs, which can be exploited as an optimization problem for the second goal, such as fatigue load alleviation. In this paper, a closed-loop model predictive controller is developed that minimizes the wind farm tracking errors, the dynamical fatigue load, and and the load equalization. The controller is evaluated in a mediumfidelity model. A 64 WTs simulation case study is used to demonstrate the control performance for different penalty factor settings. The results indicated the WF can alleviate dynamical fatigue load and have no significant impact on power tracking. However, the uneven load distribution in the wind turbine system poses challenges for maintenance. By adding a trade-off between the load equalization and dynamical fatigue load, the load differences between WTs are significantly reduced, while the dynamical fatigue load slightly increases when selecting a proper penalty factor.Comment: Accepted by Electric Power Systems Researc

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application/pdf論説departmental bulletin pape

NEW LATE JURASSIC PALEOMAGNETIC RESULTS FROM SHARILYN FORMATION, SOUTHERN MONGOLIA, AMURIA BLOCK, AND THEIR IMPLICATIONS FOR THE TECTONIC EVOLUTION OF THE MONGOL–OKHOTSK SUTURE

The Amuria block occupies the eastern part of the Central Asian Orogenic Belt between the Siberia craton and the North China block (NCB) and bears important information to understand the evolution of the MongolOkhotsk suture and the amalgamation of East Asia. However, the paleomagnetic database of Amuria remains very poor.The Amuria block occupies the eastern part of the Central Asian Orogenic Belt between the Siberia craton and the North China block (NCB) and bears important information to understand the evolution of the MongolOkhotsk suture and the amalgamation of East Asia

Integrated transparent surface acoustic wave technology for active de-fogging and icing protection on glass

There have been great concerns on poor visibility and hazardous issues due to fogging and ice/frost formation on glass surfaces of windshields, windows of vehicles/airplanes, and solar panels. Existing methods for their monitoring and removal include those active ones (such as using resistance heating) or passive ones (such as using surface icephobic treatments), which are not always applicable, effective or reliable. In this study, we proposed a novel strategy by implementing transparent thin film surface acoustic wave (SAW) devices by directly coating ZnO films onto glass substrate and studied their de-fogging, active anti-icing and de-icing mechanisms using the SAW technology. Effects of powers and wavelengths of SAW devices were investigated and influences of acousto-heating and surface hydrophobic treatments were evaluated. Results showed that de-fogging time was dramatically decreased with the increase of SAW powers when the thin film-based SAW devices were exposed to humid air flow for different durations. The icing accretion was significantly delayed under the applied SAW agitation, and SAW application has also effectively promoted de-icing on glass substrate, due to the interfacial nanoscale vibration and localized heating effect.</p

Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity

Zeroth-order optimization (ZO) is a memory-efficient strategy for fine-tuning Large Language Models using only forward passes. However, the application of ZO fine-tuning in memory-constrained settings such as mobile phones and laptops is still challenging since full precision forward passes are infeasible. In this study, we address this limitation by integrating sparsity and quantization into ZO fine-tuning of LLMs. Specifically, we investigate the feasibility of fine-tuning an extremely small subset of LLM parameters using ZO. This approach allows the majority of un-tuned parameters to be quantized to accommodate the constraint of limited device memory. Our findings reveal that the pre-training process can identify a set of "sensitive parameters" that can guide the ZO fine-tuning of LLMs on downstream tasks. Our results demonstrate that fine-tuning 0.1% sensitive parameters in the LLM with ZO can outperform the full ZO fine-tuning performance, while offering wall-clock time speedup. Additionally, we show that ZO fine-tuning targeting these 0.1% sensitive parameters, combined with 4 bit quantization, enables efficient ZO fine-tuning of an Llama2-7B model on a GPU device with less than 8 GiB of memory and notably reduced latency